Author name cluster

Eric Lucet

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers

1 author row

IROS Conference 2023 Conference Paper

Navigation Among Movable Obstacles Using Machine Learning Based Total Time Cost Optimization

Kai Zhang
Eric Lucet
Julien Alexandre Dit Sandretto
David Filliat

Most navigation approaches treat obstacles as static objects and choose to bypass them. However, the detour could be costly or could lead to failures in indoor environments. The recently developed navigation among movable obstacles (NAMO) methods prefer to remove all the movable obstacles blocking the way, which might be not the best choice when planning and moving obstacles takes a long time. We propose a pipeline where the robot solves the NAMO problems by optimizing the total time to reach the goal. This is achieved by a supervised learning approach that can predict the time of planning and performing obstacle motion before actually doing it if this leads to faster goal reaching. Besides, a pose generator based on reinforcement learning is proposed to decide where the robot can move the obstacle. The method is evaluated in two kinds of simulation environments and the results demonstrate its advantages compared to the classical bypass and obstacle removal strategies.

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ICRA Conference 2021 Conference Paper

Online velocity fluctuation of off-road wheeled mobile robots: A reinforcement learning approach

François Gauthier-Clerc
Ashley Hill
Jean Laneurit
Roland Lenain
Eric Lucet

During the off-road path following of a wheeled mobile robot in presence of poor grip conditions, the longitudinal velocity should be limited in order to maintain safe navigation with limited tracking errors, while at the same time being high enough to minimize travel time. Thus, this paper presents a new approach of online speed fluctuation, capable of limiting the lateral error below a given threshold, while maximizing the longitudinal velocity. This is accomplished using a neural network trained with a reinforcement learning method. This speed modulation is done side-by-side with an existing model-based predictive steering control, using a state estimator and dynamic observers. Simulated and experimental results show a decrease in tracking error, while maintaining a consistent travel time when compared to a classical constant speed method and to a kinematic speed fluctuation method.

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IROS Conference 2020 Conference Paper

A modified Hybrid Reciprocal Velocity Obstacles approach for multi-robot motion planning without communication

Maxime Sainte Catherine
Eric Lucet

Ensuring a safe online motion planning despite a large number of moving agents is the problem addressed in this paper. Collision avoidance is achieved without communication between the agents and without global localization system. The proposed solution is a modification of the Hybrid Reciprocal Velocity Obstacles (HRVO) combined with a tracking error estimation, in order to adapt the Velocity Obstacle paradigm to agents with kinodynamic constraints and unreliable velocity estimates. This solution, evaluated in simulation and in real test scenario with three dynamic unicycle type robots, shows an improvement over HRVO.

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IROS Conference 2020 Conference Paper

Online gain setting method for path tracking using CMA-ES: Application to off-road mobile robot control

Ashley Hill
Jean Laneurit
Roland Lenain
Eric Lucet

This paper proposes a new approach for online control law gains adaptation, through the use of neural networks and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm, in order to optimize the behavior of the robot with respect to an objective function. The neural network considered takes as input the current observed state as well as its uncertainty, and provides as output the control law gains. It is trained, using the CMA-ES algorithm, on a simulator reproducing the vehicle dynamics. Then, it is tested in real conditions on an agricultural mobile robot at different speeds. The transferability of this method from simulation to a real system is demonstrated, as well as its robustness to environmental changes, such as GPS signal degradation or ground variation. As a result, path following errors are reduced, while ensuring tracking stability.

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ICRA Conference 2017 Conference Paper

Real-time distributed receding horizon motion planning and control for mobile multi-robot dynamic systems

José M. Mendes Filho
Eric Lucet
David Filliat

This paper proposes an improvement of a motion planning approach and a modified model predictive control (MPC) for solving the navigation problem of a team of dynamical wheeled mobile robots in the presence of obstacles in a realistic environment. Planning is performed by a distributed receding horizon algorithm where constrained optimization problems are numerically solved for each prediction time-horizon. This approach allows distributed motion planning for a multi-robot system with asynchronous communication while avoiding collisions and minimizing the travel time of each robot. However, the robots dynamics prevents the planned motion to be applied directly to the robots. Using unicycle-like vehicles in a dynamic simulation, we show that deviations from the planned motion caused by the robots dynamics can be overcome by modifying the optimization problem underlying the planning algorithm and by adding an MPC for trajectory tracking. Results also indicate that this approach can be used in systems subjected to real-time constraint.

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IROS Conference 2010 Conference Paper

Accurate and stable mobile robot path tracking: An integrated solution for off-road and high speed context

Roland Lenain
Eric Lucet
Christophe Grand
Benoît Thuilot
Faïz Ben Amar

This paper is focused on the problem of accurate and reliable path tracking control of a 4-wheels car-like mobile robot moving off-road at high speed. Dynamic and extended kinematic models that take into account the effects of wheel skidding are presented. Based on the extended kinematic model, an adaptive and predictive controller for path tracking is derived. This control law is combined to a stabilization algorithm of yaw motion, based on the dynamic model and the modulation of driven wheel forces. The overall control architecture is experimentally evaluated on a slipping terrain. Results demonstrate enhanced performances as the robot succeed in following the path at high speed, accurately and without loss of control.

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IROS Conference 2009 Conference Paper

Dynamic yaw and velocity control of the 6WD skid-steering mobile robot RobuROC6 using sliding mode technique

Eric Lucet
Christophe Grand
Damien Sallé
Philippe Bidaud

A robust dynamic feedback controller is designed and implemented, based on the dynamic model of the six-wheel skid-steering RobuROC6 robot, performing high speed turns. The control inputs are respectively the linear velocity and the yaw angle. The main object of this paper is to elaborate a sliding mode controller, proved to be robust enough to ignore the knowledge of the forces within the wheel-soil interaction, in the presence of sliding phenomena and ground level fluctuations. Finally, a 3D simulation is performed with an accurate physical engine to evaluate the efficiency of this designed control law.

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ICRA Conference 2008 Conference Paper

Stabilization algorithm for a high speed car-like robot achieving steering maneuver

Eric Lucet
Christophe Grand
Damien Sallé
Philippe Bidaud

This paper deals with design and implementation of a stabilization algorithm for a car like robot performing high speed turns. The control of such a kind of system is rather difficult because of the complexity of the physical wheel- soil interaction model. In this paper, it is planned to analyze the complex dynamic model of this process to elaborate a stabilization algorithm only based on the measurement of the system yaw rate. Finally, a 3D simulation is performed to evaluate the efficiency of this designed stabilization algorithm.

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